A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods

Springer Science and Business Media LLC - Tập 79 - Trang 1-12 - 2020
Hamid Ebrahimy1, Bakhtiar Feizizadeh2,3, Saeed Salmani2, Hossein Azadi4,5,6
1Remote Sensing and GIS Research Centre, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
2Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran
3Institute of Environment, University of Tabriz, Tabriz, Iran
4Department of Geography, Ghent University, Ghent, Belgium
5Research Group Climate Change and Security, Institute of Geography, University of Hamburg, Hamburg, Germany
6Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic

Tóm tắt

Land subsidence occurrence in the Tasuj plane is becoming more frequent and hazardous in the near future due to the water crisis. To mitigate damage caused by land subsidence events, it is necessary to determine the susceptible or prone areas. This study focuses on producing and comparing land subsidence susceptibility map (LSSM) using boosted regression tree (BRT), random forest (RF), and classification and regression tree (CART) approaches with twelve influencing variables, namely altitude, slope angle, aspect, groundwater level, groundwater level change, land cover, lithology, distance to fault, distance to stream, stream power index, topographic wetness index, and plan curvature. Moreover, by implementing the Relief-F feature selection method, the most important variables in LSSM procedure were identified. The performance of the adopted methods was assessed using the area under the receiver operating characteristics curve (AUROC) and statistical evaluation indexes. The results showed that all the employed methods performed well; in particular, the BRT model (AUROC = 0.819) yielded higher prediction accuracy than RF (AUROC = 0.798) and CART (AUROC = 0.764). Findings of this study can assist in characterizing and mitigating the related hazard of land subsidence events.

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